论文标题
Sienet：图像外推的暹罗扩展网络
SiENet: Siamese Expansion Network for Image Extrapolation
论文作者
论文摘要
与图像介绍不同，图像支出在图像中心中具有相对较少的上下文，以捕获图像边框上的更多内容以预测。因此，现有方法的经典编码器管道可能无法完美预测伸出的未知内容。在本文中，提出了一种新型的两级暹罗对抗模型，用于外推，提出了名为Siamese扩展网络（Sienet）。在两个阶段，一个新颖的边界敏感卷积称为自适应填充卷积，旨在允许编码器预测未知的内容，从而减轻解码器的负担。此外，为了将先验知识介绍到网络并增强编码器的推断能力，暹罗对抗机制旨在使我们的网络为未覆盖的图像特征的覆盖远距离特征的分布建模。四个数据集上的结果表明，我们的方法的表现优于现有的最新方法，并且可以产生现实的结果。
Different from image inpainting, image outpainting has relative less context in the image center to capture and more content at the image border to predict. Therefore, classical encoder-decoder pipeline of existing methods may not predict the outstretched unknown content perfectly. In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed. In two stages, a novel border sensitive convolution named adaptive filling convolution is designed for allowing encoder to predict the unknown content, alleviating the burden of decoder. Besides, to introduce prior knowledge to network and reinforce the inferring ability of encoder, siamese adversarial mechanism is designed to enable our network to model the distribution of covered long range feature for that of uncovered image feature. The results on four datasets has demonstrated that our method outperforms existing state-of-the-arts and could produce realistic results.
